





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    BertTokenizer,
    ViltConfig,
    ViltForImageAndTextRetrieval,
    ViltForImagesAndTextClassification,
    ViltForMaskedLM,
    ViltForQuestionAnswering,
    ViltImageProcessor,
    ViltProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__							:      List[Any]         =    logging.get_logger(__name__)






def    _A	(   lowerCamelCase				,			lowerCamelCase=False				,			lowerCamelCase=False				,			lowerCamelCase=False ):
   a__						:    Optional[int]   		=	[]
   for i in range(config.num_hidden_layers ):
      # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
      rename_keys.append((F"""transformer.blocks.{i}.norm1.weight""", F"""vilt.encoder.layer.{i}.layernorm_before.weight""") )
      rename_keys.append((F"""transformer.blocks.{i}.norm1.bias""", F"""vilt.encoder.layer.{i}.layernorm_before.bias""") )
      rename_keys.append(
          (F"""transformer.blocks.{i}.attn.proj.weight""", F"""vilt.encoder.layer.{i}.attention.output.dense.weight""") )
      rename_keys.append(
          (F"""transformer.blocks.{i}.attn.proj.bias""", F"""vilt.encoder.layer.{i}.attention.output.dense.bias""") )
      rename_keys.append((F"""transformer.blocks.{i}.norm2.weight""", F"""vilt.encoder.layer.{i}.layernorm_after.weight""") )
      rename_keys.append((F"""transformer.blocks.{i}.norm2.bias""", F"""vilt.encoder.layer.{i}.layernorm_after.bias""") )
      rename_keys.append(
          (F"""transformer.blocks.{i}.mlp.fc1.weight""", F"""vilt.encoder.layer.{i}.intermediate.dense.weight""") )
      rename_keys.append((F"""transformer.blocks.{i}.mlp.fc1.bias""", F"""vilt.encoder.layer.{i}.intermediate.dense.bias""") )
      rename_keys.append((F"""transformer.blocks.{i}.mlp.fc2.weight""", F"""vilt.encoder.layer.{i}.output.dense.weight""") )
      rename_keys.append((F"""transformer.blocks.{i}.mlp.fc2.bias""", F"""vilt.encoder.layer.{i}.output.dense.bias""") )

   # embeddings
   rename_keys.extend(
       [
           # text embeddings
           ("text_embeddings.word_embeddings.weight", "vilt.embeddings.text_embeddings.word_embeddings.weight"),
           (
               "text_embeddings.position_embeddings.weight",
               "vilt.embeddings.text_embeddings.position_embeddings.weight",
           ),
           ("text_embeddings.position_ids", "vilt.embeddings.text_embeddings.position_ids"),
           (
               "text_embeddings.token_type_embeddings.weight",
               "vilt.embeddings.text_embeddings.token_type_embeddings.weight",
           ),
           ("text_embeddings.LayerNorm.weight", "vilt.embeddings.text_embeddings.LayerNorm.weight"),
           ("text_embeddings.LayerNorm.bias", "vilt.embeddings.text_embeddings.LayerNorm.bias"),
           # patch embeddings
           ("transformer.cls_token", "vilt.embeddings.cls_token"),
           ("transformer.patch_embed.proj.weight", "vilt.embeddings.patch_embeddings.projection.weight"),
           ("transformer.patch_embed.proj.bias", "vilt.embeddings.patch_embeddings.projection.bias"),
           ("transformer.pos_embed", "vilt.embeddings.position_embeddings"),
           # token type embeddings
           ("token_type_embeddings.weight", "vilt.embeddings.token_type_embeddings.weight"),
       ] )

   # final layernorm + pooler
   rename_keys.extend(
       [
           ("transformer.norm.weight", "vilt.layernorm.weight"),
           ("transformer.norm.bias", "vilt.layernorm.bias"),
           ("pooler.dense.weight", "vilt.pooler.dense.weight"),
           ("pooler.dense.bias", "vilt.pooler.dense.bias"),
       ] )

   # classifier head(s)
   if vqa_model:
      # classification head
      rename_keys.extend(
          [
              ("vqa_classifier.0.weight", "classifier.0.weight"),
              ("vqa_classifier.0.bias", "classifier.0.bias"),
              ("vqa_classifier.1.weight", "classifier.1.weight"),
              ("vqa_classifier.1.bias", "classifier.1.bias"),
              ("vqa_classifier.3.weight", "classifier.3.weight"),
              ("vqa_classifier.3.bias", "classifier.3.bias"),
          ] )
   elif nlvr_model:
      # classification head
      rename_keys.extend(
          [
              ("nlvr2_classifier.0.weight", "classifier.0.weight"),
              ("nlvr2_classifier.0.bias", "classifier.0.bias"),
              ("nlvr2_classifier.1.weight", "classifier.1.weight"),
              ("nlvr2_classifier.1.bias", "classifier.1.bias"),
              ("nlvr2_classifier.3.weight", "classifier.3.weight"),
              ("nlvr2_classifier.3.bias", "classifier.3.bias"),
          ] )
   else:
      pass

   return rename_keys






def    _A	(   lowerCamelCase				,			lowerCamelCase ):
   for i in range(config.num_hidden_layers ):
      a__						:    Optional[Any]   		=	'''vilt.'''
      # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
      a__						:    int   		=	state_dict.pop(F"""transformer.blocks.{i}.attn.qkv.weight""" )
      a__						:    Optional[int]   		=	state_dict.pop(F"""transformer.blocks.{i}.attn.qkv.bias""" )
      # next, add query, keys and values (in that order) to the state dict
      a__						:    Tuple   		=	in_proj_weight[
          : config.hidden_size, :
      ]
      a__						:    int   		=	in_proj_bias[: config.hidden_size]
      a__						:    Optional[int]   		=	in_proj_weight[
          config.hidden_size : config.hidden_size * 2, :
      ]
      a__						:    Optional[int]   		=	in_proj_bias[
          config.hidden_size : config.hidden_size * 2
      ]
      a__						:    Tuple   		=	in_proj_weight[
          -config.hidden_size :, :
      ]
      a__						:    Optional[Any]   		=	in_proj_bias[-config.hidden_size :]






def    _A	(   lowerCamelCase ):
   a__						:    Tuple   		=	['''head.weight''', '''head.bias''']
   for k in ignore_keys:
      state_dict.pop(lowerCamelCase				,			lowerCamelCase )






def    _A	(   lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase ):
   a__						:    Any   		=	dct.pop(lowerCamelCase )
   a__						:    Dict   		=	val






@torch.no_grad()
def    _A	(   lowerCamelCase				,			lowerCamelCase ):
   a__						:    Dict   		=	ViltConfig(image_size=384				,			patch_size=32				,			tie_word_embeddings=lowerCamelCase )
   a__						:    int   		=	False
   a__						:    Union[str, Any]   		=	False
   a__						:    Optional[int]   		=	False
   a__						:    Tuple   		=	False
   if "vqa" in checkpoint_url:
      a__						:    Union[str, Any]   		=	True
      a__						:    Dict   		=	3129
      a__						:    Union[str, Any]   		=	'''huggingface/label-files'''
      a__						:    Optional[Any]   		=	'''vqa2-id2label.json'''
      a__						:    List[Any]   		=	json.load(open(hf_hub_download(lowerCamelCase				,			lowerCamelCase				,			repo_type="dataset" )				,			"r" ) )
      a__						:    str   		=	{int(lowerCamelCase ): v for k, v in idalabel.items()}
      a__						:    int   		=	idalabel
      a__						:    Optional[int]   		=	{v: k for k, v in idalabel.items()}
      a__						:    Optional[int]   		=	ViltForQuestionAnswering(lowerCamelCase )
   elif "nlvr" in checkpoint_url:
      a__						:    Dict   		=	True
      a__						:    Optional[Any]   		=	2
      a__						:    List[str]   		=	{0: '''False''', 1: '''True'''}
      a__						:    Optional[int]   		=	{v: k for k, v in config.idalabel.items()}
      a__						:    Optional[int]   		=	3
      a__						:    str   		=	ViltForImagesAndTextClassification(lowerCamelCase )
   elif "irtr" in checkpoint_url:
      a__						:    Optional[Any]   		=	True
      a__						:    List[Any]   		=	ViltForImageAndTextRetrieval(lowerCamelCase )
   elif "mlm_itm" in checkpoint_url:
      a__						:    int   		=	True
      a__						:    int   		=	ViltForMaskedLM(lowerCamelCase )
   else:
      raise ValueError("Unknown model type" )

   # load state_dict of original model, remove and rename some keys
   a__						:    Any   		=	torch.hub.load_state_dict_from_url(lowerCamelCase				,			map_location="cpu" )['''state_dict''']
   a__						:    str   		=	create_rename_keys(lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase )
   for src, dest in rename_keys:
      rename_key(lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase )
   read_in_q_k_v(lowerCamelCase				,			lowerCamelCase )
   if mlm_model or irtr_model:
      a__						:    Any   		=	['''itm_score.fc.weight''', '''itm_score.fc.bias''']
      for k in ignore_keys:
         state_dict.pop(lowerCamelCase				,			lowerCamelCase )

    # load state dict into HuggingFace model
   model.eval()
   if mlm_model:
      a__						:    List[str]   		=	model.load_state_dict(lowerCamelCase				,			strict=lowerCamelCase )
      assert missing_keys == ["mlm_score.decoder.bias"]
   else:
      model.load_state_dict(lowerCamelCase )

   # Define processor
   a__						:    Optional[Any]   		=	ViltImageProcessor(size=384 )
   a__						:    int   		=	BertTokenizer.from_pretrained("bert-base-uncased" )
   a__						:    Optional[int]   		=	ViltProcessor(lowerCamelCase				,			lowerCamelCase )

   # Forward pass on example inputs (image + text)
   if nlvr_model:
      a__						:    Union[str, Any]   		=	Image.open(requests.get("https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg"				,			stream=lowerCamelCase ).raw )
      a__						:    Optional[Any]   		=	Image.open(requests.get("https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg"				,			stream=lowerCamelCase ).raw )
      a__						:    List[str]   		=	(
          '''The left image contains twice the number of dogs as the right image, and at least two dogs in total are'''
          ''' standing.'''
      )
      a__						:    Union[str, Any]   		=	processor(lowerCamelCase				,			lowerCamelCase				,			return_tensors="pt" )
      a__						:    Tuple   		=	processor(lowerCamelCase				,			lowerCamelCase				,			return_tensors="pt" )
      a__						:    List[str]   		=	model(
          input_ids=encoding_a.input_ids				,			pixel_values=encoding_a.pixel_values				,			pixel_values_a=encoding_a.pixel_values				,			)
   else:
      a__						:    Any   		=	Image.open(requests.get("http://images.cocodataset.org/val2017/000000039769.jpg"				,			stream=lowerCamelCase ).raw )
      if mlm_model:
         a__						:    int   		=	'''a bunch of [MASK] laying on a [MASK].'''
      else:
         a__						:    Any   		=	'''How many cats are there?'''
      a__						:    Tuple   		=	processor(lowerCamelCase				,			lowerCamelCase				,			return_tensors="pt" )
      a__						:    List[str]   		=	model(**lowerCamelCase )

   # Verify outputs
   if mlm_model:
      a__						:    List[Any]   		=	torch.Size([1, 11, 3_0522] )
      a__						:    List[str]   		=	torch.tensor([-12.5061, -12.5123, -12.5174] )
      assert outputs.logits.shape == expected_shape
      assert torch.allclose(outputs.logits[0, 0, :3]				,			lowerCamelCase				,			atol=1E-4 )

      # verify masked token prediction equals "cats"
      a__						:    str   		=	outputs.logits[0, 4, :].argmax(-1 ).item()
      assert tokenizer.decode([predicted_id] ) == "cats"
   elif vqa_model:
      a__						:    Tuple   		=	torch.Size([1, 3129] )
      a__						:    Tuple   		=	torch.tensor([-15.9495, -18.1472, -10.3041] )
      assert torch.allclose(outputs.logits[0, :3]				,			lowerCamelCase				,			atol=1E-4 )
      assert outputs.logits.shape == expected_shape
      assert torch.allclose(outputs.logits[0, 0, :3]				,			lowerCamelCase				,			atol=1E-4 )

      # verify vqa prediction equals "2"
      a__						:    str   		=	outputs.logits.argmax(-1 ).item()
      assert model.config.idalabel[predicted_idx] == "2"
   elif nlvr_model:
      a__						:    int   		=	torch.Size([1, 2] )
      a__						:    List[str]   		=	torch.tensor([-2.8721, 2.1291] )
      assert torch.allclose(outputs.logits[0, :3]				,			lowerCamelCase				,			atol=1E-4 )
      assert outputs.logits.shape == expected_shape

   Path(lowerCamelCase ).mkdir(exist_ok=lowerCamelCase )
   print(F"""Saving model and processor to {pytorch_dump_folder_path}""" )
   model.save_pretrained(lowerCamelCase )
   processor.save_pretrained(lowerCamelCase )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE__							:      Any         =    argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--checkpoint_url""",
      default="""https://github.com/dandelin/ViLT/releases/download/200k/vilt_200k_mlm_itm.ckpt""",
      type=str,
      help="""URL of the checkpoint you\'d like to convert.""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
  )

  SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    parser.parse_args()
  convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)









'''simple docstring'''





import argparse
import collections
import json
import os
import re
import string
import sys

import numpy as np


_UpperCamelCase						:						List[str]  =					re.compile(R'\b(a|an|the)\b', re.UNICODE)

_UpperCamelCase						:						Optional[int]  =					None
def __UpperCAmelCase   (					)    ->   Optional[Any]:
			UpperCAmelCase_      :       List[Any]          =       argparse.ArgumentParser('''Official evaluation script for SQuAD version 2.0.'''		)
			parser.add_argument('''data_file'''			,       metavar='''data.json'''			,       help='''Input data JSON file.'''		)
			parser.add_argument('''pred_file'''			,       metavar='''pred.json'''			,       help='''Model predictions.'''		)
			parser.add_argument(
			    '''--out-file'''			,       '''-o'''			,       metavar='''eval.json'''			,       help='''Write accuracy metrics to file (default is stdout).'''		)
			parser.add_argument(
			    '''--na-prob-file'''			,       '''-n'''			,       metavar='''na_prob.json'''			,       help='''Model estimates of probability of no answer.'''		)
			parser.add_argument(
			    '''--na-prob-thresh'''			,       '''-t'''			,       type=A			,       default=1.0			,       help='''Predict "" if no-answer probability exceeds this (default = 1.0).'''			,       )
			parser.add_argument(
			    '''--out-image-dir'''			,       '''-p'''			,       metavar='''out_images'''			,       default=A			,       help='''Save precision-recall curves to directory.'''		)
			parser.add_argument('''--verbose'''			,       '''-v'''			,       action='''store_true'''		)
			if len(sys.argv		) == 1:
						parser.print_help()
						sys.exit(1		)
			return parser.parse_args()
def __UpperCAmelCase   (					A					:			int		)    ->   Optional[Any]:
			UpperCAmelCase_      :       Optional[Any]          =       {}
			for article in dataset:
						for p in article["paragraphs"]:
									for qa in p["qas"]:
												UpperCAmelCase_      :       Union[str, Any]          =       bool(qa['''answers''']['''text''']		)
			return qid_to_has_ans
def __UpperCAmelCase   (					A					:			List[Any]		)    ->   Dict:

			def remove_articles(A					:			Dict		):
						return ARTICLES_REGEX.sub(''' '''			,       A		)

			def white_space_fix(A					:			str		):
						return " ".join(text.split()		)

			def remove_punc(A					:			Tuple		):
						UpperCAmelCase_      :       Dict          =       set(string.punctuation		)
						return "".join(ch for ch in text if ch not in exclude		)

			def lower(A					:			Any		):
						return text.lower()

			return white_space_fix(remove_articles(remove_punc(lower(A		)		)		)		)
def __UpperCAmelCase   (					A					:			Optional[Any]		)    ->   Tuple:
			if not s:
						return []
			return normalize_answer(A		).split()
def __UpperCAmelCase   (					A					:			Optional[int]			,       A					:			Dict		)    ->   List[str]:
			return int(normalize_answer(A		) == normalize_answer(A		)		)
def __UpperCAmelCase   (					A					:			Optional[Any]			,       A					:			List[Any]		)    ->   str:
			UpperCAmelCase_      :       Tuple          =       get_tokens(A		)
			UpperCAmelCase_      :       Union[str, Any]          =       get_tokens(A		)
			UpperCAmelCase_      :       Dict          =       collections.Counter(A		) & collections.Counter(A		)
			UpperCAmelCase_      :       Optional[int]          =       sum(common.values()		)
			if len(A		) == 0 or len(A		) == 0:
						# If either is no-answer, then F1 is 1 if they agree, 0 otherwise
						return int(gold_toks == pred_toks		)
			if num_same == 0:
						return 0
			UpperCAmelCase_      :       int          =       1.0 * num_same / len(A		)
			UpperCAmelCase_      :       Any          =       1.0 * num_same / len(A		)
			UpperCAmelCase_      :       Any          =       (2 * precision * recall) / (precision + recall)
			return fa
def __UpperCAmelCase   (					A					:			Union[str, Any]			,       A					:			str		)    ->   Union[str, Any]:
			UpperCAmelCase_      :       List[Any]          =       {}
			UpperCAmelCase_      :       str          =       {}
			for article in dataset:
						for p in article["paragraphs"]:
									for qa in p["qas"]:
												UpperCAmelCase_      :       Optional[Any]          =       qa['''id''']
												UpperCAmelCase_      :       List[str]          =       [t for t in qa['''answers''']['''text'''] if normalize_answer(A		)]
												if not gold_answers:
															# For unanswerable questions, only correct answer is empty string
															UpperCAmelCase_      :       Tuple          =       ['''''']
												if qid not in preds:
															print(F"Missing prediction for {qid}"		)
															continue
												UpperCAmelCase_      :       Tuple          =       preds[qid]
												# Take max over all gold answers
												UpperCAmelCase_      :       List[Any]          =       max(compute_exact(A			,       A		) for a in gold_answers		)
												UpperCAmelCase_      :       int          =       max(compute_fa(A			,       A		) for a in gold_answers		)
			return exact_scores, fa_scores
def __UpperCAmelCase   (					A					:			Optional[int]			,       A					:			Tuple			,       A					:			Optional[Any]			,       A					:			List[Any]		)    ->   Optional[int]:
			UpperCAmelCase_      :       Tuple          =       {}
			for qid, s in scores.items():
						UpperCAmelCase_      :       Union[str, Any]          =       na_probs[qid] > na_prob_thresh
						if pred_na:
									UpperCAmelCase_      :       List[str]          =       float(not qid_to_has_ans[qid]		)
						else:
									UpperCAmelCase_      :       Any          =       s
			return new_scores
def __UpperCAmelCase   (					A					:			Any			,       A					:			Any			,       A					:			str=None		)    ->   int:
			if not qid_list:
						UpperCAmelCase_      :       str          =       len(A		)
						return collections.OrderedDict(
						    [
						        ('''exact''', 100.0 * sum(exact_scores.values()		) / total),
						        ('''f1''', 100.0 * sum(fa_scores.values()		) / total),
						        ('''total''', total),
						    ]		)
			else:
						UpperCAmelCase_      :       Dict          =       len(A		)
						return collections.OrderedDict(
						    [
						        ('''exact''', 100.0 * sum(exact_scores[k] for k in qid_list		) / total),
						        ('''f1''', 100.0 * sum(fa_scores[k] for k in qid_list		) / total),
						        ('''total''', total),
						    ]		)
def __UpperCAmelCase   (					A					:			Union[str, Any]			,       A					:			Dict			,       A					:			Union[str, Any]		)    ->   Dict:
			for k in new_eval:
						UpperCAmelCase_      :       str          =       new_eval[k]
def __UpperCAmelCase   (					A					:			str			,       A					:			Tuple			,       A					:			Any			,       A					:			List[str]		)    ->   Optional[int]:
			plt.step(A			,       A			,       color='''b'''			,       alpha=0.2			,       where='''post'''		)
			plt.fill_between(A			,       A			,       step='''post'''			,       alpha=0.2			,       color='''b'''		)
			plt.xlabel('''Recall'''		)
			plt.ylabel('''Precision'''		)
			plt.xlim([0.0, 1.05]		)
			plt.ylim([0.0, 1.05]		)
			plt.title(A		)
			plt.savefig(A		)
			plt.clf()
def __UpperCAmelCase   (					A					:			Optional[Any]			,       A					:			Optional[Any]			,       A					:			Optional[int]			,       A					:			Optional[int]			,       A					:			str=None			,       A					:			Any=None		)    ->   Any:
			UpperCAmelCase_      :       Optional[Any]          =       sorted(A			,       key=lambda A		: na_probs[k]		)
			UpperCAmelCase_      :       int          =       0.0
			UpperCAmelCase_      :       List[Any]          =       1.0
			UpperCAmelCase_      :       Optional[int]          =       0.0
			UpperCAmelCase_      :       Optional[Any]          =       [1.0]
			UpperCAmelCase_      :       List[Any]          =       [0.0]
			UpperCAmelCase_      :       Dict          =       0.0
			for i, qid in enumerate(A		):
						if qid_to_has_ans[qid]:
									true_pos += scores[qid]
						UpperCAmelCase_      :       List[Any]          =       true_pos / float(i + 1		)
						UpperCAmelCase_      :       Union[str, Any]          =       true_pos / float(A		)
						if i == len(A		) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]:
									# i.e., if we can put a threshold after this point
									avg_prec += cur_p * (cur_r - recalls[-1])
									precisions.append(A		)
									recalls.append(A		)
			if out_image:
						plot_pr_curve(A			,       A			,       A			,       A		)
			return {"ap": 100.0 * avg_prec}
def __UpperCAmelCase   (					A					:			List[Any]			,       A					:			str			,       A					:			Any			,       A					:			Optional[int]			,       A					:			Any			,       A					:			List[str]		)    ->   List[Any]:
			if out_image_dir and not os.path.exists(A		):
						os.makedirs(A		)
			UpperCAmelCase_      :       Any          =       sum(1 for v in qid_to_has_ans.values() if v		)
			if num_true_pos == 0:
						return
			UpperCAmelCase_      :       Union[str, Any]          =       make_precision_recall_eval(
			    A			,       A			,       A			,       A			,       out_image=os.path.join(A			,       '''pr_exact.png'''		)			,       title='''Precision-Recall curve for Exact Match score'''			,       )
			UpperCAmelCase_      :       List[str]          =       make_precision_recall_eval(
			    A			,       A			,       A			,       A			,       out_image=os.path.join(A			,       '''pr_f1.png'''		)			,       title='''Precision-Recall curve for F1 score'''			,       )
			UpperCAmelCase_      :       Dict          =       {k: float(A		) for k, v in qid_to_has_ans.items()}
			UpperCAmelCase_      :       Optional[int]          =       make_precision_recall_eval(
			    A			,       A			,       A			,       A			,       out_image=os.path.join(A			,       '''pr_oracle.png'''		)			,       title='''Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)'''			,       )
			merge_eval(A			,       A			,       '''pr_exact'''		)
			merge_eval(A			,       A			,       '''pr_f1'''		)
			merge_eval(A			,       A			,       '''pr_oracle'''		)
def __UpperCAmelCase   (					A					:			List[str]			,       A					:			Tuple			,       A					:			str			,       A					:			List[Any]		)    ->   Dict:
			if not qid_list:
						return
			UpperCAmelCase_      :       List[Any]          =       [na_probs[k] for k in qid_list]
			UpperCAmelCase_      :       Union[str, Any]          =       np.ones_like(A		) / float(len(A		)		)
			plt.hist(A			,       weights=A			,       bins=2_0			,       range=(0.0, 1.0)		)
			plt.xlabel('''Model probability of no-answer'''		)
			plt.ylabel('''Proportion of dataset'''		)
			plt.title(F"Histogram of no-answer probability: {name}"		)
			plt.savefig(os.path.join(A			,       F"na_prob_hist_{name}.png"		)		)
			plt.clf()
def __UpperCAmelCase   (					A					:			str			,       A					:			Dict			,       A					:			List[str]			,       A					:			int		)    ->   Optional[Any]:
			UpperCAmelCase_      :       Tuple          =       sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k]		)
			UpperCAmelCase_      :       Optional[Any]          =       num_no_ans
			UpperCAmelCase_      :       int          =       cur_score
			UpperCAmelCase_      :       List[Any]          =       0.0
			UpperCAmelCase_      :       Dict          =       sorted(A			,       key=lambda A		: na_probs[k]		)
			for i, qid in enumerate(A		):
						if qid not in scores:
									continue
						if qid_to_has_ans[qid]:
									UpperCAmelCase_      :       Tuple          =       scores[qid]
						else:
									if preds[qid]:
												UpperCAmelCase_      :       Optional[int]          =       -1
									else:
												UpperCAmelCase_      :       int          =       0
						cur_score += diff
						if cur_score > best_score:
									UpperCAmelCase_      :       Dict          =       cur_score
									UpperCAmelCase_      :       Dict          =       na_probs[qid]
			return 100.0 * best_score / len(A		), best_thresh
def __UpperCAmelCase   (					A					:			int			,       A					:			List[Any]			,       A					:			Union[str, Any]			,       A					:			Tuple			,       A					:			str			,       A					:			Union[str, Any]		)    ->   Tuple:
			UpperCAmelCase_    ,      UpperCAmelCase_      :       List[Any]          =       find_best_thresh(A			,       A			,       A			,       A		)
			UpperCAmelCase_    ,      UpperCAmelCase_      :       Optional[int]          =       find_best_thresh(A			,       A			,       A			,       A		)
			UpperCAmelCase_      :       List[str]          =       best_exact
			UpperCAmelCase_      :       List[Any]          =       exact_thresh
			UpperCAmelCase_      :       Any          =       best_fa
			UpperCAmelCase_      :       str          =       fa_thresh
def __UpperCAmelCase   (					)    ->   Tuple:
			with open(OPTS.data_file		) as f:
						UpperCAmelCase_      :       Optional[int]          =       json.load(A		)
						UpperCAmelCase_      :       str          =       dataset_json['''data''']
			with open(OPTS.pred_file		) as f:
						UpperCAmelCase_      :       List[Any]          =       json.load(A		)
			if OPTS.na_prob_file:
						with open(OPTS.na_prob_file		) as f:
									UpperCAmelCase_      :       List[str]          =       json.load(A		)
			else:
						UpperCAmelCase_      :       List[str]          =       {k: 0.0 for k in preds}
			UpperCAmelCase_      :       Optional[Any]          =       make_qid_to_has_ans(A		)  # maps qid to True/False
			UpperCAmelCase_      :       Optional[int]          =       [k for k, v in qid_to_has_ans.items() if v]
			UpperCAmelCase_      :       Dict          =       [k for k, v in qid_to_has_ans.items() if not v]
			UpperCAmelCase_    ,      UpperCAmelCase_      :       List[Any]          =       get_raw_scores(A			,       A		)
			UpperCAmelCase_      :       Optional[int]          =       apply_no_ans_threshold(A			,       A			,       A			,       OPTS.na_prob_thresh		)
			UpperCAmelCase_      :       Tuple          =       apply_no_ans_threshold(A			,       A			,       A			,       OPTS.na_prob_thresh		)
			UpperCAmelCase_      :       int          =       make_eval_dict(A			,       A		)
			if has_ans_qids:
						UpperCAmelCase_      :       Optional[int]          =       make_eval_dict(A			,       A			,       qid_list=A		)
						merge_eval(A			,       A			,       '''HasAns'''		)
			if no_ans_qids:
						UpperCAmelCase_      :       Dict          =       make_eval_dict(A			,       A			,       qid_list=A		)
						merge_eval(A			,       A			,       '''NoAns'''		)
			if OPTS.na_prob_file:
						find_all_best_thresh(A			,       A			,       A			,       A			,       A			,       A		)
			if OPTS.na_prob_file and OPTS.out_image_dir:
						run_precision_recall_analysis(A			,       A			,       A			,       A			,       A			,       OPTS.out_image_dir		)
						histogram_na_prob(A			,       A			,       OPTS.out_image_dir			,       '''hasAns'''		)
						histogram_na_prob(A			,       A			,       OPTS.out_image_dir			,       '''noAns'''		)
			if OPTS.out_file:
						with open(OPTS.out_file			,       '''w'''		) as f:
									json.dump(A			,       A		)
			else:
						print(json.dumps(A			,       indent=2		)		)


if __name__ == "__main__":
						_UpperCamelCase						:						Union[str, Any]  =					parse_args()
						if OPTS.out_image_dir:
												import matplotlib

												matplotlib.use('Agg')
												import matplotlib.pyplot as plt
						main()


